Overview

Dataset statistics

Number of variables20
Number of observations553649
Missing cells255332
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory84.5 MiB
Average record size in memory160.0 B

Variable types

Numeric8
Categorical10
Boolean2

Alerts

FLAG_MOBIL has constant value "1" Constant
CNT_CHILDREN is highly correlated with CNT_FAM_MEMBERSHigh correlation
CNT_FAM_MEMBERS is highly correlated with CNT_CHILDRENHigh correlation
CNT_CHILDREN is highly correlated with CNT_FAM_MEMBERSHigh correlation
CNT_FAM_MEMBERS is highly correlated with CNT_CHILDRENHigh correlation
CNT_CHILDREN is highly correlated with CNT_FAM_MEMBERSHigh correlation
CNT_FAM_MEMBERS is highly correlated with CNT_CHILDRENHigh correlation
OCCUPATION_TYPE is highly correlated with FLAG_MOBIL and 1 other fieldsHigh correlation
NAME_INCOME_TYPE is highly correlated with FLAG_MOBILHigh correlation
FLAG_PHONE is highly correlated with FLAG_MOBILHigh correlation
FLAG_OWN_REALTY is highly correlated with FLAG_MOBILHigh correlation
NAME_FAMILY_STATUS is highly correlated with FLAG_MOBILHigh correlation
FLAG_OWN_CAR is highly correlated with FLAG_MOBILHigh correlation
FLAG_WORK_PHONE is highly correlated with FLAG_MOBILHigh correlation
FLAG_EMAIL is highly correlated with FLAG_MOBILHigh correlation
NAME_EDUCATION_TYPE is highly correlated with FLAG_MOBILHigh correlation
FLAG_MOBIL is highly correlated with OCCUPATION_TYPE and 10 other fieldsHigh correlation
NAME_HOUSING_TYPE is highly correlated with FLAG_MOBILHigh correlation
CODE_GENDER is highly correlated with OCCUPATION_TYPE and 1 other fieldsHigh correlation
CODE_GENDER is highly correlated with FLAG_OWN_CAR and 1 other fieldsHigh correlation
FLAG_OWN_CAR is highly correlated with CODE_GENDERHigh correlation
CNT_CHILDREN is highly correlated with CNT_FAM_MEMBERSHigh correlation
NAME_INCOME_TYPE is highly correlated with AGEHigh correlation
OCCUPATION_TYPE is highly correlated with CODE_GENDERHigh correlation
CNT_FAM_MEMBERS is highly correlated with CNT_CHILDRENHigh correlation
AGE is highly correlated with NAME_INCOME_TYPEHigh correlation
OCCUPATION_TYPE has 170608 (30.8%) missing values Missing
MONTHS_BALANCE has 84724 (15.3%) missing values Missing
CNT_CHILDREN has 387335 (70.0%) zeros Zeros
YEARS_EMPLOYED has 94948 (17.1%) zeros Zeros
MONTHS_BALANCE has 13392 (2.4%) zeros Zeros
STATUS has 187137 (33.8%) zeros Zeros

Reproduction

Analysis started2022-04-14 19:01:29.395717
Analysis finished2022-04-14 19:03:06.279606
Duration1 minute and 36.88 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

ID
Real number (ℝ≥0)

Distinct116469
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5221598.844
Minimum5008804
Maximum7995770
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-04-14T15:03:06.523396image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum5008804
5-th percentile5021656
Q15047637
median5091335
Q35135972
95-th percentile6230229
Maximum7995770
Range2986966
Interquartile range (IQR)88335

Descriptive statistics

Standard deviation385684.2599
Coefficient of variation (CV)0.07386324983
Kurtosis6.163223595
Mean5221598.844
Median Absolute Deviation (MAD)44637
Skewness2.659772059
Sum2.890932978 × 1012
Variance1.487523483 × 1011
MonotonicityNot monotonic
2022-04-14T15:03:06.742584image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
511596061
 
< 0.1%
508965061
 
< 0.1%
508968361
 
< 0.1%
503715361
 
< 0.1%
511819261
 
< 0.1%
509547061
 
< 0.1%
501094361
 
< 0.1%
502915861
 
< 0.1%
510568261
 
< 0.1%
511757661
 
< 0.1%
Other values (116459)553039
99.9%
ValueCountFrequency (%)
500880416
< 0.1%
50088053
 
< 0.1%
500880630
< 0.1%
50088085
 
< 0.1%
50088095
 
< 0.1%
500881024
< 0.1%
500881124
< 0.1%
500881217
< 0.1%
50088137
 
< 0.1%
50088143
 
< 0.1%
ValueCountFrequency (%)
79957701
< 0.1%
79652481
< 0.1%
78369021
< 0.1%
78235951
< 0.1%
77444401
< 0.1%
77280661
< 0.1%
77028331
< 0.1%
77022381
< 0.1%
76189361
< 0.1%
75834861
< 0.1%

CODE_GENDER
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
F
364348 
M
189301 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM
2nd rowM
3rd rowM
4th rowM
5th rowM

Common Values

ValueCountFrequency (%)
F364348
65.8%
M189301
34.2%

Length

2022-04-14T15:03:06.941750image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T15:03:07.052421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
f364348
65.8%
m189301
34.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FLAG_OWN_CAR
Boolean

HIGH CORRELATION
HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size540.8 KiB
False
342813 
True
210836 
ValueCountFrequency (%)
False342813
61.9%
True210836
38.1%
2022-04-14T15:03:07.116735image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

FLAG_OWN_REALTY
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size540.8 KiB
True
368911 
False
184738 
ValueCountFrequency (%)
True368911
66.6%
False184738
33.4%
2022-04-14T15:03:07.176600image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

CNT_CHILDREN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4218286315
Minimum0
Maximum19
Zeros387335
Zeros (%)70.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-04-14T15:03:07.279810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum19
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7439695718
Coefficient of variation (CV)1.763677276
Kurtosis22.57659933
Mean0.4218286315
Median Absolute Deviation (MAD)0
Skewness2.674298427
Sum233545
Variance0.5534907238
MonotonicityNot monotonic
2022-04-14T15:03:07.469065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0387335
70.0%
1109521
 
19.8%
248849
 
8.8%
36695
 
1.2%
4881
 
0.2%
5241
 
< 0.1%
1478
 
< 0.1%
739
 
< 0.1%
196
 
< 0.1%
62
 
< 0.1%
Other values (2)2
 
< 0.1%
ValueCountFrequency (%)
0387335
70.0%
1109521
 
19.8%
248849
 
8.8%
36695
 
1.2%
4881
 
0.2%
5241
 
< 0.1%
62
 
< 0.1%
739
 
< 0.1%
91
 
< 0.1%
121
 
< 0.1%
ValueCountFrequency (%)
196
 
< 0.1%
1478
 
< 0.1%
121
 
< 0.1%
91
 
< 0.1%
739
 
< 0.1%
62
 
< 0.1%
5241
 
< 0.1%
4881
 
0.2%
36695
 
1.2%
248849
8.8%

AMT_INCOME_TOTAL
Real number (ℝ≥0)

Distinct866
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean184791.9778
Minimum26100
Maximum6750000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-04-14T15:03:07.698147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum26100
5-th percentile72000
Q1117000
median157500
Q3225000
95-th percentile360000
Maximum6750000
Range6723900
Interquartile range (IQR)108000

Descriptive statistics

Standard deviation101336.2185
Coefficient of variation (CV)0.548379966
Kurtosis64.22692669
Mean184791.9778
Median Absolute Deviation (MAD)45000
Skewness3.547587469
Sum1.023098937 × 1011
Variance1.026902918 × 1010
MonotonicityNot monotonic
2022-04-14T15:03:07.921979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13500064256
 
11.6%
18000047971
 
8.7%
15750047278
 
8.5%
11250046028
 
8.3%
22500042995
 
7.8%
20250034166
 
6.2%
9000027434
 
5.0%
27000025353
 
4.6%
31500014738
 
2.7%
6750013920
 
2.5%
Other values (856)189510
34.2%
ValueCountFrequency (%)
261001
 
< 0.1%
2700082
< 0.1%
279002
 
< 0.1%
283501
 
< 0.1%
28723.51
 
< 0.1%
288001
 
< 0.1%
291331
 
< 0.1%
2925047
< 0.1%
3015065
< 0.1%
306001
 
< 0.1%
ValueCountFrequency (%)
67500001
 
< 0.1%
45000001
 
< 0.1%
3950059.51
 
< 0.1%
38250001
 
< 0.1%
33750001
 
< 0.1%
31500001
 
< 0.1%
22141171
 
< 0.1%
20250003
< 0.1%
18900002
< 0.1%
18000002
< 0.1%

NAME_INCOME_TYPE
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Working
282413 
Commercial associate
130394 
Pensioner
95504 
State servant
45190 
Student
 
148

Length

Max length20
Median length7
Mean length10.89645786
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWorking
2nd rowWorking
3rd rowWorking
4th rowWorking
5th rowWorking

Common Values

ValueCountFrequency (%)
Working282413
51.0%
Commercial associate130394
23.6%
Pensioner95504
 
17.2%
State servant45190
 
8.2%
Student148
 
< 0.1%

Length

2022-04-14T15:03:08.123185image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T15:03:08.235408image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
working282413
38.7%
commercial130394
17.9%
associate130394
17.9%
pensioner95504
 
13.1%
state45190
 
6.2%
servant45190
 
6.2%
student148
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NAME_EDUCATION_TYPE
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Secondary / secondary special
379642 
Higher education
146949 
Incomplete higher
 
20450
Lower secondary
 
6196
Academic degree
 
412

Length

Max length29
Median length29
Mean length24.939216
Min length15

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHigher education
2nd rowHigher education
3rd rowHigher education
4th rowHigher education
5th rowHigher education

Common Values

ValueCountFrequency (%)
Secondary / secondary special379642
68.6%
Higher education146949
 
26.5%
Incomplete higher20450
 
3.7%
Lower secondary6196
 
1.1%
Academic degree412
 
0.1%

Length

2022-04-14T15:03:08.377331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T15:03:08.492985image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
secondary765480
41.0%
379642
20.3%
special379642
20.3%
higher167399
 
9.0%
education146949
 
7.9%
incomplete20450
 
1.1%
lower6196
 
0.3%
academic412
 
< 0.1%
degree412
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NAME_FAMILY_STATUS
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
Married
382670 
Single / not married
69919 
Civil marriage
44677 
Separated
 
32644
Widow
 
23739

Length

Max length20
Median length7
Mean length9.238775831
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCivil marriage
2nd rowCivil marriage
3rd rowCivil marriage
4th rowCivil marriage
5th rowCivil marriage

Common Values

ValueCountFrequency (%)
Married382670
69.1%
Single / not married69919
 
12.6%
Civil marriage44677
 
8.1%
Separated32644
 
5.9%
Widow23739
 
4.3%

Length

2022-04-14T15:03:08.643244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T15:03:08.774789image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
married452589
56.0%
single69919
 
8.7%
69919
 
8.7%
not69919
 
8.7%
civil44677
 
5.5%
marriage44677
 
5.5%
separated32644
 
4.0%
widow23739
 
2.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

NAME_HOUSING_TYPE
Categorical

HIGH CORRELATION

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
House / apartment
496580 
With parents
 
24683
Municipal apartment
 
18369
Rented apartment
 
7324
Office apartment
 
4367

Length

Max length19
Median length17
Mean length16.81392543
Min length12

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRented apartment
2nd rowRented apartment
3rd rowRented apartment
4th rowRented apartment
5th rowRented apartment

Common Values

ValueCountFrequency (%)
House / apartment496580
89.7%
With parents24683
 
4.5%
Municipal apartment18369
 
3.3%
Rented apartment7324
 
1.3%
Office apartment4367
 
0.8%
Co-op apartment2326
 
0.4%

Length

2022-04-14T15:03:08.926754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T15:03:09.040686image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
apartment528966
33.0%
house496580
31.0%
496580
31.0%
with24683
 
1.5%
parents24683
 
1.5%
municipal18369
 
1.1%
rented7324
 
0.5%
office4367
 
0.3%
co-op2326
 
0.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FLAG_MOBIL
Categorical

CONSTANT
HIGH CORRELATION
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
1
553649 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1553649
100.0%

Length

2022-04-14T15:03:09.185303image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T15:03:09.293450image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
1553649
100.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FLAG_WORK_PHONE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
0
430843 
1
122806 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0430843
77.8%
1122806
 
22.2%

Length

2022-04-14T15:03:09.395370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T15:03:09.658350image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0430843
77.8%
1122806
 
22.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FLAG_PHONE
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
0
390891 
1
162758 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0390891
70.6%
1162758
29.4%

Length

2022-04-14T15:03:09.806328image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T15:03:09.908816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0390891
70.6%
1162758
29.4%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

FLAG_EMAIL
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size4.2 MiB
0
503217 
1
50432 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0503217
90.9%
150432
 
9.1%

Length

2022-04-14T15:03:10.033695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-04-14T15:03:10.155380image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
ValueCountFrequency (%)
0503217
90.9%
150432
 
9.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

OCCUPATION_TYPE
Categorical

HIGH CORRELATION
HIGH CORRELATION
MISSING

Distinct18
Distinct (%)< 0.1%
Missing170608
Missing (%)30.8%
Memory size4.2 MiB
Laborers
95859 
Core staff
52290 
Sales staff
51157 
Managers
46593 
Drivers
35649 
Other values (13)
101493 

Length

Max length21
Median length10
Mean length10.49062894
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecurity staff
2nd rowSecurity staff
3rd rowSecurity staff
4th rowSecurity staff
5th rowSecurity staff

Common Values

ValueCountFrequency (%)
Laborers95859
17.3%
Core staff52290
 
9.4%
Sales staff51157
 
9.2%
Managers46593
 
8.4%
Drivers35649
 
6.4%
High skill tech staff21924
 
4.0%
Accountants18404
 
3.3%
Medicine staff18089
 
3.3%
Cooking staff10093
 
1.8%
Security staff9878
 
1.8%
Other values (8)23105
 
4.2%
(Missing)170608
30.8%

Length

2022-04-14T15:03:10.296641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
staff180477
29.3%
laborers98625
16.0%
core52290
 
8.5%
sales51157
 
8.3%
managers46593
 
7.6%
drivers35649
 
5.8%
high21924
 
3.6%
skill21924
 
3.6%
tech21924
 
3.6%
accountants18404
 
3.0%
Other values (13)66671
 
10.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

CNT_FAM_MEMBERS
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.194200658
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-04-14T15:03:10.478042image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile4
Maximum20
Range19
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9088151052
Coefficient of variation (CV)0.4141896057
Kurtosis8.223231439
Mean2.194200658
Median Absolute Deviation (MAD)0
Skewness1.357786315
Sum1214817
Variance0.8259448954
MonotonicityNot monotonic
2022-04-14T15:03:10.655960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2300066
54.2%
1104850
 
18.9%
395009
 
17.2%
446116
 
8.3%
56413
 
1.2%
6879
 
0.2%
7189
 
< 0.1%
1578
 
< 0.1%
939
 
< 0.1%
206
 
< 0.1%
Other values (3)4
 
< 0.1%
ValueCountFrequency (%)
1104850
 
18.9%
2300066
54.2%
395009
 
17.2%
446116
 
8.3%
56413
 
1.2%
6879
 
0.2%
7189
 
< 0.1%
82
 
< 0.1%
939
 
< 0.1%
111
 
< 0.1%
ValueCountFrequency (%)
206
 
< 0.1%
1578
 
< 0.1%
141
 
< 0.1%
111
 
< 0.1%
939
 
< 0.1%
82
 
< 0.1%
7189
 
< 0.1%
6879
 
0.2%
56413
 
1.2%
446116
8.3%

AGE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct16379
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.13554696
Minimum20.50418558
Maximum68.99799448
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-04-14T15:03:10.870007image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum20.50418558
5-th percentile27.33252565
Q134.69201969
median43.18500722
Q353.58084013
95-th percentile63.00745395
Maximum68.99799448
Range48.49380891
Interquartile range (IQR)18.88882044

Descriptive statistics

Standard deviation11.36108862
Coefficient of variation (CV)0.257413568
Kurtosis-1.039538549
Mean44.13554696
Median Absolute Deviation (MAD)9.317097545
Skewness0.1541479961
Sum24435601.44
Variance129.0743346
MonotonicityNot monotonic
2022-04-14T15:03:11.114002image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41.45191209419
 
0.1%
40.15688207416
 
0.1%
42.91669233398
 
0.1%
38.70305345378
 
0.1%
32.79465013377
 
0.1%
27.86367961354
 
0.1%
38.69483973348
 
0.1%
40.86599999346
 
0.1%
55.15513666327
 
0.1%
46.25967679324
 
0.1%
Other values (16369)549962
99.3%
ValueCountFrequency (%)
20.504185581
 
< 0.1%
21.021651
 
< 0.1%
21.027125811
 
< 0.1%
21.049029071
 
< 0.1%
21.079146051
 
< 0.1%
21.095573495
 
< 0.1%
21.144855811
 
< 0.1%
21.147593721
 
< 0.1%
21.2379446530
< 0.1%
21.257111
 
< 0.1%
ValueCountFrequency (%)
68.997994481
 
< 0.1%
68.883002391
 
< 0.1%
68.8638370451
< 0.1%
68.861099131
 
< 0.1%
68.8309821656
< 0.1%
68.778961921
 
< 0.1%
68.721465881
 
< 0.1%
68.7187279732
< 0.1%
68.702300531
 
< 0.1%
68.6886109933
< 0.1%

YEARS_EMPLOYED
Real number (ℝ≥0)

ZEROS

Distinct9406
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.09114325
Minimum0
Maximum47.99824774
Zeros94948
Zeros (%)17.1%
Negative0
Negative (%)0.0%
Memory size4.2 MiB
2022-04-14T15:03:11.321154image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.054094198
median4.26292121
Q38.761302422
95-th percentile19.8881565
Maximum47.99824774
Range47.99824774
Interquartile range (IQR)7.707208225

Descriptive statistics

Standard deviation6.585870321
Coefficient of variation (CV)1.081220725
Kurtosis3.737188483
Mean6.09114325
Median Absolute Deviation (MAD)3.657843761
Skewness1.741275298
Sum3372355.369
Variance43.37368789
MonotonicityNot monotonic
2022-04-14T15:03:11.552142image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
094948
 
17.1%
1.09790071922
 
0.2%
0.2956939568828
 
0.1%
4.213638884784
 
0.1%
4.794075169779
 
0.1%
0.5475814014751
 
0.1%
5.212974941720
 
0.1%
6.929642635677
 
0.1%
4.961087497661
 
0.1%
0.8460132652660
 
0.1%
Other values (9396)451919
81.6%
ValueCountFrequency (%)
094948
17.1%
0.032854884082
 
< 0.1%
0.035592791092
 
< 0.1%
0.043806512115
 
< 0.1%
0.0465444191221
 
< 0.1%
0.049282326131
 
< 0.1%
0.052020233131
 
< 0.1%
0.060233954152
 
< 0.1%
0.062971861161
 
< 0.1%
0.065709768172
 
< 0.1%
ValueCountFrequency (%)
47.998247741
 
< 0.1%
45.906486791
 
< 0.1%
45.161776082
 
< 0.1%
44.805848171
 
< 0.1%
44.745614221
 
< 0.1%
44.408851651
 
< 0.1%
44.176129561
 
< 0.1%
44.088516531
 
< 0.1%
43.423205131
 
< 0.1%
43.020732818
< 0.1%

MONTHS_BALANCE
Real number (ℝ)

MISSING
ZEROS

Distinct61
Distinct (%)< 0.1%
Missing84724
Missing (%)15.3%
Infinite0
Infinite (%)0.0%
Mean-20.97951272
Minimum-60
Maximum0
Zeros13392
Zeros (%)2.4%
Negative455533
Negative (%)82.3%
Memory size4.2 MiB
2022-04-14T15:03:11.759466image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-48
Q1-32
median-19
Q3-8
95-th percentile-1
Maximum0
Range60
Interquartile range (IQR)24

Descriptive statistics

Standard deviation14.68052272
Coefficient of variation (CV)-0.6997551812
Kurtosis-0.7197345295
Mean-20.97951272
Median Absolute Deviation (MAD)11
Skewness-0.4838080213
Sum-9837818
Variance215.5177474
MonotonicityNot monotonic
2022-04-14T15:03:11.951305image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-113474
 
2.4%
-213398
 
2.4%
013392
 
2.4%
-313237
 
2.4%
-413119
 
2.4%
-512949
 
2.3%
-612827
 
2.3%
-712606
 
2.3%
-812359
 
2.2%
-912224
 
2.2%
Other values (51)339340
61.3%
(Missing)84724
 
15.3%
ValueCountFrequency (%)
-60290
 
0.1%
-59550
 
0.1%
-58838
 
0.2%
-571081
 
0.2%
-561342
0.2%
-551631
0.3%
-541915
0.3%
-532162
0.4%
-522480
0.4%
-512821
0.5%
ValueCountFrequency (%)
013392
2.4%
-113474
2.4%
-213398
2.4%
-313237
2.4%
-413119
2.4%
-512949
2.3%
-612827
2.3%
-712606
2.3%
-812359
2.2%
-912224
2.2%

STATUS
Real number (ℝ)

ZEROS

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.6104842599
Minimum-1
Maximum5
Zeros187137
Zeros (%)33.8%
Negative355798
Negative (%)64.3%
Memory size4.2 MiB
2022-04-14T15:03:12.115269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile-1
Q1-1
median-1
Q30
95-th percentile0
Maximum5
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5989766712
Coefficient of variation (CV)-0.9811500649
Kurtosis18.56858721
Mean-0.6104842599
Median Absolute Deviation (MAD)0
Skewness2.76919863
Sum-337994
Variance0.3587730527
MonotonicityNot monotonic
2022-04-14T15:03:12.250173image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
-1355798
64.3%
0187137
33.8%
18166
 
1.5%
51282
 
0.2%
2778
 
0.1%
3280
 
0.1%
4208
 
< 0.1%
ValueCountFrequency (%)
-1355798
64.3%
0187137
33.8%
18166
 
1.5%
2778
 
0.1%
3280
 
0.1%
4208
 
< 0.1%
51282
 
0.2%
ValueCountFrequency (%)
51282
 
0.2%
4208
 
< 0.1%
3280
 
0.1%
2778
 
0.1%
18166
 
1.5%
0187137
33.8%
-1355798
64.3%

Interactions

2022-04-14T15:02:57.333201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:36.462057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:39.369179image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:42.059096image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:45.166344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:48.022176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:50.910567image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:54.060200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:57.754894image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:36.966716image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:39.681775image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:42.420657image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:45.540488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:48.351613image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:51.267492image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:54.439325image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:58.123068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:37.310624image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:40.020024image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:42.750053image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:45.905065image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:48.744215image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:51.662106image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:54.849273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:58.490633image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:37.675034image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:40.358469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:43.100148image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:46.311621image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:49.084688image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:52.010099image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:55.220289image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:58.912026image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:38.013340image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:40.699870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:43.787165image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:46.681003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:49.446394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:52.352784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:55.618377image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:59.242001image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:38.360060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:41.022414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:44.116236image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:47.016695image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:49.814488image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:52.922641image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:56.015562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:59.612521image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:38.704870image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:41.335740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:44.429370image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:47.330550image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:50.167902image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:53.290084image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:56.408941image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:59.990956image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:39.048535image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:41.670421image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:44.797201image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:47.695685image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:50.562765image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:53.703923image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-04-14T15:02:56.811971image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-04-14T15:03:12.413345image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-04-14T15:03:12.698081image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-04-14T15:03:12.985184image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-04-14T15:03:13.491468image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-04-14T15:03:13.832156image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-04-14T15:03:00.635585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-04-14T15:03:02.284257image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-04-14T15:03:04.483003image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-04-14T15:03:05.359699image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

IDCODE_GENDERFLAG_OWN_CARFLAG_OWN_REALTYCNT_CHILDRENAMT_INCOME_TOTALNAME_INCOME_TYPENAME_EDUCATION_TYPENAME_FAMILY_STATUSNAME_HOUSING_TYPEFLAG_MOBILFLAG_WORK_PHONEFLAG_PHONEFLAG_EMAILOCCUPATION_TYPECNT_FAM_MEMBERSAGEYEARS_EMPLOYEDMONTHS_BALANCESTATUS
05008804MYY0427500.0WorkingHigher educationCivil marriageRented apartment1100NaN2.032.86857412.4355740.0-1
15008804MYY0427500.0WorkingHigher educationCivil marriageRented apartment1100NaN2.032.86857412.435574-1.0-1
25008804MYY0427500.0WorkingHigher educationCivil marriageRented apartment1100NaN2.032.86857412.435574-2.0-1
35008804MYY0427500.0WorkingHigher educationCivil marriageRented apartment1100NaN2.032.86857412.435574-3.0-1
45008804MYY0427500.0WorkingHigher educationCivil marriageRented apartment1100NaN2.032.86857412.435574-4.0-1
55008804MYY0427500.0WorkingHigher educationCivil marriageRented apartment1100NaN2.032.86857412.435574-5.0-1
65008804MYY0427500.0WorkingHigher educationCivil marriageRented apartment1100NaN2.032.86857412.435574-6.0-1
75008804MYY0427500.0WorkingHigher educationCivil marriageRented apartment1100NaN2.032.86857412.435574-7.0-1
85008804MYY0427500.0WorkingHigher educationCivil marriageRented apartment1100NaN2.032.86857412.435574-8.0-1
95008804MYY0427500.0WorkingHigher educationCivil marriageRented apartment1100NaN2.032.86857412.435574-9.0-1

Last rows

IDCODE_GENDERFLAG_OWN_CARFLAG_OWN_REALTYCNT_CHILDRENAMT_INCOME_TOTALNAME_INCOME_TYPENAME_EDUCATION_TYPENAME_FAMILY_STATUSNAME_HOUSING_TYPEFLAG_MOBILFLAG_WORK_PHONEFLAG_PHONEFLAG_EMAILOCCUPATION_TYPECNT_FAM_MEMBERSAGEYEARS_EMPLOYEDMONTHS_BALANCESTATUS
5536396836737FNN067500.0PensionerSecondary / secondary specialWidowHouse / apartment1000NaN1.059.6097110.000000NaN-1
5536406836990MNN0360000.0Commercial associateHigher educationSeparatedHouse / apartment1000Managers1.044.8304891.659172NaN-1
5536416837235FNY0135000.0WorkingSecondary / secondary specialMarriedHouse / apartment1000Laborers2.037.2054182.992532NaN-1
5536426837264FNN290000.0State servantHigher educationSingle / not marriedHouse / apartment1000Core staff4.043.9762623.490831NaN-1
5536436837452MNN1135000.0WorkingSecondary / secondary specialSeparatedHouse / apartment1000Security staff2.035.5161300.490085NaN-1
5536446837707MNY0202500.0WorkingHigher educationCivil marriageHouse / apartment1100Laborers2.036.9891246.321827NaN-1
5536456839651FNY399000.0PensionerSecondary / secondary specialSingle / not marriedHouse / apartment1000NaN1.051.5602650.000000NaN-1
5536466839917FNY0180000.0PensionerHigher educationMarriedHouse / apartment1000NaN2.030.0238887.403301NaN-1
5536476840104MNY0135000.0PensionerSecondary / secondary specialSeparatedHouse / apartment1000NaN1.062.1970330.000000NaN-1
5536486840222FNN0103500.0WorkingSecondary / secondary specialSingle / not marriedHouse / apartment1000Laborers1.043.6395008.232886NaN-1